The present invention relates to systems and methods for transmitting and receiving remote sensor data and, more particularly, but not by way of limitation, to distributed systems and processes for acquiring remote data via communication protocols that enhance or optimize data transmission and validation speeds while reducing the amount of data required for transmission.
Industrial machines or assets, generally, are engineered to perform particular tasks as part of a business enterprise or process. Such assets may include, among other things, gas and steam turbines that drive power plants, wind turbines that generate electricity on wind farms, various types of manufacturing equipment on production lines, aircraft and train engines, and the drilling equipment used in mining operations. As will be appreciated, the efficient implementation of any of these types of industrial assets is a complex design challenge, which, to be successful, must anticipate both the physics of the task at hand as well as the environment in which the assets are expected to operate.
As part of this implementation, software and hardware-based controllers have long been the preferred solution for driving the operation of industrial assets. However, with the rise of inexpensive cloud computing, increasing sensor capabilities and decreasing sensor costs, as well as the proliferation of mobile technologies and networking capabilities, new possibilities have arisen to reshape how industrial assets are designed, operated, and maintained. Specifically, recent advances in sensor technologies now enable the harvesting of new types and vastly more operational data, while progress in network speed and capacity allows essentially real-time transmission of this data to distant locations. This means, for example, that even for a geographical dispersed fleet of like industrial assets, the increased amounts of data gathered at each remote site may be efficiently brought together, analyzed, and employed in ways aimed at improving both fleet and individual asset performance. As a consequence of this evolving and data-intensive environment, new opportunities arise to enhance the value of industrial assets through novel industrial-focused hardware and software solutions.
As a result, there is a significant need for efficient ways to gather and transmit sensor data to remote locations. It is common for such sensing systems to have a multitude of sensors, each of which measures a particular operating parameter or changes to such parameter. These sensors may be remotely located relative a computerized controller intended to respond to the data received from the sensors. For example, remote monitoring of gas turbines, especially industrial gas turbines, has become increasingly common. Technicians employed by a manufacturer of the gas turbine may remotely analyze information regarding the operation of the gas turbine and prescribe corrective steps, such as parts replacements or operational adjustments, which may then be performed by on-site operators. Remotely analyzing and diagnosing data collected from a gas turbine and computing accurate information regarding the combustion dynamic levels of the gas turbine becomes a useful enabler for above activities.
In general, prior art sensor networks rely on central monitoring units and require complex signal processing arrangements and processes to effectively manage data generated by sensors. Given the ever-increasing levels of data being generated by new sensing technologies, the efficient transmission of data and, more particularly, the efficient transmission of sensor data for timely use in analytics presents an ongoing challenge.